Mobile Semiconductor’s Enhanced Memory Compilers Improve Power On Edge AI Devices

By AIT News Desk On Jul 17, 2020

Mobile Semiconductor, a Seattle WA based company, announced a new generation of Ultra Low Leakage (ULL) and Ultra Low Power (ULP) SRAM Memory Compilers that dramatically improve the performance to power ratio for Edge AI chips

Cameron Fisher, CEO and Founder of Mobile Semiconductor, said, “Drawing on the expertise we acquired developing our successful Compilers on GF’s 22FDX platform, we fully expect that these new generations of Ultra Low Leakage and Ultra Low Power Memory Compilers will meet and exceed our customer’s expectations in Edge AI.”

These new ULL and ULP Memory Compliers on GF’s 22FDX platform are unique in the industry and come with the same high-quality design and testing that customers have come to expect from Mobile Semiconductor. With leakage numbers in the nano-amp range, memory designed using Mobile Semiconductor’s compilers lead the industry.

Zenity Introduces the First Comprehensive AI Security Posture Management Solution to Govern Microsoft Fabric AI Skills

Dec 24, 2024

CodeGate Protects The Privacy Of Developers That Use AI Coding Assistants

Dec 24, 2024

AI is Only 30% Away From Matching Human-Level General Intelligence on GAIA Benchmark

Dec 24, 2024

Prev Next 1 of 40,451

The ULL and ULP Compilers improve the performance to power ration making them ideal for Edge AI and Machine Learning applications which are measured in tera-operations per watt (TOPS/WATT) and host new power modes giving the engineer maximum flexibility.

The graph above shows the relative power vs performance relationship between the ULP/ULL compilers optimized for Edge applications and the Ultra High Speed memory compiler which is optimized for cloud-based servers.

Fisher continued, “Mobile Semiconductor remains the leader in providing low power solutions and these compliers remove the roadblocks that engineers have struggled with in past designs. Almost every edge product developed for Edge AI, Machine Learning and IoT will have new and more stringent power demands placed on them. We believe that Mobile Semiconductor is meeting the low power needs of this market.”